Optimizing the deployment of virtual resources and automating post-deployment actions in a cloud environment

ABSTRACT

A computer-implemented method includes: monitoring, by a computing device, performance of currently deployed virtual machines (VMs) that implement particular services; determining, by the computing device, optimal configuration options for deployment of new VMs that implement one or more of the particular services based on the monitoring the performance of the currently deployed VMs; and outputting, by the computing device, information regarding the optimal configuration options to a user requesting the deployment of a new VM implementing one or more of the particular services.

BACKGROUND

The present invention generally relates to cloud services, and moreparticularly, to optimizing the deployment of virtual resources andautomating post-deployment actions in a cloud environment.

Cloud providers provide users with virtual resources as part of virtualmachines. Users submit requests (e.g., as part of a service-levelagreement (SLA)) for virtual machines having a particular configuration(e.g., minimum virtual CPU resources, memory resources, storageresources, etc.) Users can also customize each virtual image for theirindividual purpose at deploy time or later after being deployed. Anexample of such customization may be the installation of additionalservices or middleware. Building a virtual machine with a particularconfiguration takes time to deploy. The perceived deployment time forvirtual machines can be a significant differentiator between cloudproviders, but there are technical limitations as to how quickly adeployment can occur.

Typically, it is important for system and management stability that theuser not be able to adjust their SLA (e.g., the virtual resourcesrequested) while the virtual machine is running. Thus, it is typicallycritical that the user select an appropriate resource reservation atdeploy time. However redeployment often occurs to adjust the amount ofvirtual resources for the user. For example, redeployment of a virtualmachine occurs when a user initially requests too few or too manyresources. This may happen since public cloud users often are motivatedby pay-as-you-go cost structure to minimize resource usage and privatecloud users are motivated by trying to conserve a scarce publicresource.

Further, resource fragmentation can occurs when a common problem with aVM propagates across the deployment of multiple similar VMs. Thefragmentation problem can occur regardless of the natural lifetime of aVM, but fragmentation is made worse when requested resources areinappropriate for the intended purpose of the VM. The stability of theentire system of a cloud provider can be compromised when a VM isredeployed with a change in resources. If fragmentation is notimmediately apparent, then allocation of a limited resource can lead tofractions of the global resource bucket being distributed amongst many.When a resource fraction is returned, it may not be appropriately sizedfor future resource requests. For example, a returned resource maypossibly be too small, and unusable for future applications.Alternatively, a returned resource may be too large, thereby leading topotentially more fragmentation when a small fraction is leftover afterbeing used.

Memory and hard drives have partially solved this by defragmentationtechniques to physically relocate used resources in a way to make theusage continuous. While defragmentation may be possible for a singleresource, cloud environments include multiple resource types (e.g.,minimum virtual CPU resources, memory resources, storage resources,etc.), each being a variable at deploy time. This multi-commodityproblem in cloud environments can make resource fragmentationexponentially worse and can lead to severe resource underutilization andeven can lead to the point of cloud instability. This instability can bereached when there are sufficient resources to deploy the requested VM,but none of those resources are available on the same physical machine.

Cloud environments, e.g., data centers, utilize IT professionals tomanage their VMs. For example, IT professionals keep software up to dateand protected from viruses, ensuring a VM maintains the correct useraccounts, are time synchronized, performing OS and SW key management,ensuring communication services up and running, etc. With traditionaldata centers, adding a new server or making changes is limited to theavailability of IT professionals. When moving to the cloud, the creationof new servers is simplified and resources potentially are betterutilized, but many management tasks for VMs are either no longerperformed or again limited by IT professional's time. For somebusinesses, neither is acceptable and ultimately this restricts theircontinued business growth.

Managed Services seeks to automate as many of these tasks as possible toreduce the number of IT professionals required. With automation,detailed records and auditing can be performed to prove a level ofcompliance too. Generation of these automated tasks is a laborious joband can be a one-size-fits-none situation. For example, a softwarepackage is added to all VMs, but once receiving the VM, many users thenmodify and/or remove the package. Updating of the automated tasks mayonly occur when a user notifies an IT professional or administrator ofthe update.

SUMMARY

In an aspect of the invention, computer-implemented method includes:monitoring, by a computing device, performance of currently deployedvirtual machines (VMs) that implement particular services; determining,by the computing device, optimal configuration options for deployment ofnew VMs that implement one or more of the particular services based onthe monitoring the performance of the currently deployed VMs; andoutputting, by the computing device, information regarding the optimalconfiguration options to a user requesting the deployment of a new VMimplementing one or more of the particular services. In a furtheraspect, the method includes monitoring usage activity of the currentlydeployed VMs, where the determining the optimal configuration options isfurther based on the usage activity. In a further aspect, the methodfurther includes pre-deploying one or more VMs.

In an aspect of the invention, there is a computer program productcomprising a computer readable storage medium having programinstructions embodied therewith. The program instructions are executableby a computing device to cause the computing device to: store or monitorinformation regarding popularity of virtual machine (VM) configurationsof deployed virtual machines (VMs); determine costs associated withbuilding VMs having a particular configuration on-demand; score VMconfigurations based on the measure of popularity and the costsassociated with building the VMs on-demand; select VM configurationswith scores satisfying a threshold for pre-deployment; and store theselected VM configurations for pre-deployment. In a further aspect, theprogram instructions cause the computing device to provide a VM having apre-deployed configuration to a requesting user. In a further aspect,the determining the costs includes: determining time or resourcesconsumed when building VMs having the particular configurationon-demand; assigning weightings to the resources consumed based on thetype of resources consumed; and determining the resources consumed basedon the weightings.

In an aspect of the invention, a system includes: a CPU, a computerreadable memory and a computer readable storage medium associated with acomputing device, The system also includes: program instructions tomonitor modifications made to deployed virtual machines (VM) havingparticular characteristics; program instructions to determine techniquesused to implement the modifications; program instructions to generate orupdate an automated action to modify other VMs having the particularcharacteristics, where the automated action includes the determinedtechniques used to implement the modifications; and program instructionsto store or update the automated action in a catalog of automatedactions, wherein the automated actions in the catalog of automatedactions are selectable to implement a modification to a deployed VM. Theprogram instructions are stored on the computer readable storage mediumfor execution by the CPU via the computer readable memory.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is described in the detailed description whichfollows, in reference to the noted plurality of drawings by way ofnon-limiting examples of exemplary embodiments of the present invention.

FIG. 1 depicts a cloud computing node according to an embodiment of thepresent invention.

FIG. 2 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 3 depicts abstraction model layers according to an embodiment ofthe present invention.

FIG. 4 shows an example environment in accordance with aspects of thepresent invention.

FIG. 5 shows a block diagram of example components of an optimalconfiguration and catalog component in accordance with aspects of thepresent invention.

FIG. 6 shows an example flowchart for presenting optimal configurationoptions to a requesting user of a new VM deployment in accordance withaspects of the present invention.

FIG. 7 shows an example flowchart for pre-deploying VMs with particularconfigurations in accordance with aspects of the present invention.

FIG. 8 shows an example flowchart for pre-deploying VMs with particularconfigurations in accordance with aspects of the present invention.

DETAILED DESCRIPTION

The present invention generally relates to cloud services, and moreparticularly, to optimizing the deployment of virtual resources andautomating post-deployment actions in a cloud environment. In accordancewith aspects of the present invention, systems and/or methods may storeinformation regarding virtual machine (VM) configurations (e.g., theresources provided by VMs associated with users), monitor theperformance and activity (e.g., common usage patterns) of the VMspost-deployment, and periodically determine optimal configurationoptions for future VM deployment requests based on the performance andactivity of currently deployed VMs. In additional and/or alternativeaspects of the present invention, the systems and/or methods maypre-deploy VMs to reduce the amount of time and/or cost associated withserving a VM to a user. Aspects of the present invention may assistusers in selecting appropriate resource allocations at deployment,thereby saving the user time in redeployments and reconfigurations. Forexample, users can potentially save money by having appropriately sizedVMs (e.g., VMs with an appropriate amount of resources) without payingfor more resources needed.

Aspects of the present invention may identify a source of a performanceproblem in VMs, rather than identifying merely the symptoms of aproblem. Configurations can be suggested so that performance problems ina deployed VM can be avoided, thereby reducing the number ofredeployments. This can help reduce the redeployment effort, and reduceresource the costs and efforts associated with resource fragmentationcaused by reemployment of VMs. In alternative embodiments, aspects ofthe present invention may also auto-generate automated tasks anddynamically adjust the automated task options available to users overtime as the activity of VMs change.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowcharts and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowcharts may represent a module, segment, or portion of instructions,which comprises one or more executable instructions for implementing thespecified logical function(s). In some alternative implementations, thefunctions noted in the block may occur out of the order noted in thefigures. For example, two blocks shown in succession may, in fact, beexecuted substantially concurrently, or the blocks may sometimes beexecuted in the reverse order, depending upon the functionalityinvolved. It will also be noted that each block of the flowchartillustrations, and combinations of blocks in the flowchartillustrations, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts or carry outcombinations of special purpose hardware and computer instructions.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a nonremovable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 2 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and optimal configuration and catalogcomponent 96.

Referring back to FIG. 1, the Program/utility 40 may include one or moreprogram modules 42 that generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.Specifically, the program modules 42 may monitor the performance andactivity of the VMs post-deployment, and periodically computer anddetermine the optimal configurations for future VMs based on theperformance and activity of deployed VMs. Other functionalities of theprogram modules 42 are described further herein such that the programmodules 42 are not limited to the functions described above. Moreover,it is noted that some of the modules 42 can be implemented within theinfrastructure shown in FIGS. 1-3. For example, the modules 42 may berepresentative of an optimal configuration and catalog component 96.

FIG. 4 shows an overview of an example environment in accordance withaspects of the present invention. As shown in FIG. 4, environment 400may include client servers 210, a cloud provider network 215, virtualmachines 220, and an optimal configuration and catalog component 96.

The client servers 210 may include computing devices associated withusers that access the cloud provider network 215 for cloud services. Theclient servers 210 may implement any number or type of application, andmay request virtual resources provided by one or more virtual machines220 implemented as part of the cloud provider network 215. Each clientserver 210 may be associated with a service level agreement (SLA) thatdefines the configuration for a VM owned by the user of the clientserver 210. For example, the configuration may identify the resourcesprovided the VM, the types of applications supported by VM should,and/or other specifications relating to the VM.

The cloud provider network 215 may include network nodes, such asnetwork nodes 10 of FIG. 2. Additionally, or alternatively, the cloudprovider network 215 may include one or more wired and/or wirelessnetworks. For example, the cloud provider network 215 may include acellular network (e.g., a second generation (2G) network, a thirdgeneration (3G) network, a fourth generation (4G) network, a fifthgeneration (5G) network, a long-term evolution (LTE) network, a globalsystem for mobile (GSM) network, a code division multiple access (CDMA)network, an evolution-data optimized (EVDO) network, or the like), apublic land mobile network (PLMN), and/or another network. Additionally,or alternatively, the cloud provider network 215 may include a localarea network (LAN), a wide area network (WAN), a metropolitan network(MAN), the Public Switched Telephone Network (PSTN), an ad hoc network,a managed Internet Protocol (IP) network, a virtual private network(VPN), an intranet, the Internet, a fiber optic-based network, and/or acombination of these or other types of networks.

The cloud provider network 215 may implement one or more virtualmachines 220 across its network. Each virtual machine 220 may host oneor more virtual images for use by the client servers 210. Inembodiments, the client servers 210 may access virtual resourcesprovided by the virtual machines 220. One or more physical server orcomputing devices within the cloud provider network 215 may host one ormore virtual machines 220.

The optimal configuration and catalog component 96 may include one ormore computing devices that may store information regarding VMconfigurations (e.g., the resources provided by VMs for each user oreach client server 210), monitor the performance and activity of the VMspost-deployment, and periodically computer and determine the optimalconfigurations for future VMs based on the performance and activity ofdeployed VMs. The optimal configuration and catalog component 96 mayperiodically determine optimal VM configurations for future VMdeployments based on the historical performance and activity ofcurrently deployed VMs. The deployment of optimal VM configurations mayreduce the number of VM redeployments, thereby reducing costs andresource fragmentation. In embodiments, the optimal configuration andcatalog component 96 may pre-deploy VMs by storing images ofpre-deployed VMs so that these VMs do not need to be built from scratch,thereby saving time in the deployment (or redeployment) of new VMs tousers.

In alternative embodiments, the optimal configuration and catalogcomponent 96 may monitor the modification of VMs post-deployment,determine scripts/techniques used to implement the modifications,generate or update automated actions to automate modifications that areroutinely performed, and store the automated actions in a catalog foruse by administrators.

The quantity of devices and/or networks in the environment 400 is notlimited to what is shown in FIG. 4. In practice, the environment 400 mayinclude additional devices and/or networks; fewer devices and/ornetworks; different devices and/or networks; or differently arrangeddevices and/or networks than illustrated in FIG. 4. Also, in someimplementations, one or more of the devices of the environment 400 mayperform one or more functions described as being performed by anotherone or more of the devices of the environment 400. Devices of theenvironment 400 may interconnect via wired connections, wirelessconnections, or a combination of wired and wireless connections.

FIG. 5 shows a block diagram of example components of an optimalconfiguration and catalog component 96 in accordance with aspects of thepresent invention. As shown in FIG. 5, the optimal configuration andcatalog component 96 may include a VM configurations repository 510, aperformance and usage monitoring module 520, an optimal configurationdetermination module 530, a pre-deployment module and repository 540, apost-deployment activity monitoring module 550, and automated servicesmodule 560. In embodiments, the optimal configuration and catalogcomponent 96 may include additional or fewer components than those shownin FIG. 5. In embodiments, one or more of the components in FIG. 5 maycorrespond to program module 42 of FIG. 1. In embodiments, separatecomponents may be integrated into a single computing component ormodule. Additionally, or alternatively, a single component may beimplemented as multiple computing components or modules.

The VM configurations repository 510 may include a data storage device(e.g., storage system 34 of FIG. 1) that stores information regardingthe configurations of VMs currently deployed to users. For example, theVM configurations repository 510 may store metadata or informationregarding the configuration, characteristics, or attributes of the VMs.Examples of the information regarding the configuration,characteristics, or attributes of a VM may include:

-   -   Base VM (e.g., a type of pre-configured VM upon which the VM is        built).    -   The type of services implemented or supported by the VM.    -   Current resources allocated or provided by the VM.    -   Current resources utilized by the user of the VM.    -   Historical utilization peaks and valleys.

The configuration data for a VM may be stored by the VM configurationsrepository 510 when the VM is built and/or deployed to a user, and maybe updated over a period time as the VM is used by the user.

The performance and usage monitoring module 520 may include a programmodule (e.g., a program module 42 of FIG. 1) that monitors theperformance and usage of post-deployment (e.g., currently active) VMs.The performance and usage monitoring module 520 may perform periodichealth-checks on VMs, identify VMs that are re-deployed, and theconfigurations of re-deployed VMs. Also, the performance and usagemonitoring module 520 may monitor the usage of VMs, and map the usageinformation to the configuration of the VMs. Information regarding theperformance and usage patterns of VMs may be used to determine optimalconfiguration options for future VM deployment requests.

The optimal configuration determination module 530 may include a programmodule (e.g., a program module 42 of FIG. 1) that determines optimalconfiguration options for future VM deployment requests. In embodiments,the optimal configuration determination module 530 may determine theoptimal configuration options based on the characteristics of VMs(stored by the VM configurations repository 510), and the performanceand usage patterns determined by the performance and usage monitoringmodule 520. As described herein, VMs with “optimal configuration” mayreduce the number of VM redeployments, thereby improving cloud systemperformance and fragmentation.

In embodiments, the optimal configuration determination module 530 mayuse search space dimensionality reduction and/or statistical techniques,such as regression analysis, to estimate the relationship betweendifferent variables (e.g., characteristics or attributes of differentVMs). Using such techniques, for example, particular characteristics ofthe VMs (as identified and stored in the VM configurations repository510), may be isolated based on their impact on other characteristics(e.g., performance). When a characteristic in the current dataset haslimited impact on performance, then that characteristic can be removedfrom the current consideration, hence reducing the dimensionality of thesearch space. Impact levels may be a tuning parameter (either fixed ordynamic) in order to arrive at a desired target granularity of thesearch space.

Once the dimensionality of the search space has been reduced to adesired level, the optimal configuration determination module 530 mayidentify or down select configuration candidates (e.g., possibleconfiguration options for future VM deployments). The optimalconfiguration determination module 530 may group currently deployed VMsbased on their characteristics/attributes (e.g., VMs that offer similarservices). In embodiments, the optimal configuration determinationmodule 530 may discard those groups of VMs, from consideration ofpossible configuration options that have a level of popularity less thana particular threshold. In embodiments, the optimal configurationdetermination module 530 may identify optimal configuration candidatesbased on:

-   -   A threshold number of VMs with a particular configuration or in        a particular group.    -   A tiered threshold in which, for example, a particular        configuration is automatically selected as a candidate optimal        configuration when a threshold number of VMs with that        particular configuration are currently deployed, and another        configuration is selected as a potential candidate optimal        configuration when a smaller threshold number of VMs with that        configuration are currently deployed. The potential candidate        optimal configuration may later be selected as a candidate after        passing performance tests. This provides the opportunity for        certain configurations with growing popularity to potentially be        selected as a candidate optimal configuration.    -   A purer statistical approach using a stream of configuration        updates to drive a Markov model of the system, hence with system        defined probabilities, candidates may be promoted or demoted        over time.

In embodiments, the optimal configuration determination module 530 mayfurther down select the optimal configuration candidates and identifyoptimal configuration options for future VM deployments based on:

-   -   Serially testing each VM in a group of VMs having similar        characteristics for performance to determine one or more optimal        resource configurations for the VM.    -   Determining that a VM configuration satisfies performance        thresholds and performs for the users as desired.    -   Determining that a VM configuration satisfies cloud management        metrics with an emphasis on creating commonly sized sets of VM        resources such that several VMs with different characteristics        may use the same base set of resources. Common resource sizes        may help prevent resource fragmentation within the cloud        infrastructure, simplify management and deploy, and may be used        to pre-deploy VMs with particular configurations.    -   Evaluating each VM's historical performance, exclude those        configurations that resulted in unacceptable performance levels,        and of those configurations that remain, prioritize those        configurations that are the similar to other optimal resource        configurations. The resulting optimal choice may be a resource        configuration that may have been previously known to, or a new        configuration not previously known or available.    -   Testing each VM by deploying a new one to test performance        against a mock workload, and retaining the configurations that        satisfy a performance measurement threshold.    -   Using neural networks, Monte Carlo simulations, and/or other        similar statistical packages to test known VM configurations        and/or to input multiple configurations simultaneously such that        the configurations each would be a decision option, hence        resulting in a framework that may result in an identifying        optimal configuration not previously identified.

In embodiments, the optimal configuration determination module 530 maypresent optimal configuration options to a user requesting thedeployment of a new VM. For example, the optimal configurationdetermination module 530 may receive a selection, from a user, for abase VM and a set of services that the user desires to utilize using theVM. The optimal configuration determination module 530 may then presentoptimal configuration options for the user based on the selected base VMand the set of services. For example, the optimal configurationdetermination module 530 may present the optimal configuration optionsidentified as described above. In embodiments, the optimal configurationdetermination module 530 may present the configuration options alongwith respective performance statistics of VMs having similarconfigurations. In embodiments, the user may select a one of the optimalconfiguration options, or may have the freedom to select a different orcustomized configuration. As described herein, the selection of anoptimal configuration option may reduce the probability of redeploymentof the VM, thereby improving the performance of a cloud system andreducing fragmentation.

The pre-deployment module and repository 540 may include a programmodule and/or a storage system (e.g., a program module 42 and/or astorage system 34 of FIG. 1) that may “pre-deploy” VMs having particularcharacteristics and particular configurations. In embodiments, thepre-deployment module and repository 540 may pre-deploy a VM by storingthe VM so that the pre-deployed VM can be provided to a requesting userin a relatively and substantially shorter amount of time than a VM thatwould need to be built from scratch. In embodiments, the pre-deploymentmodule and repository 540 may select candidate configurations forpre-deployment. In embodiments, the candidate configurations forpre-deployment may be optimal configurations (e.g., as identified by theoptimal configuration determination module 530 as described above).However, since pre-deployment of VMs consumes resources, not all optimalconfigurations may be pre-deployed, as pre-deploying certain VMconfigurations may be more costly in relation to the benefit. Also,certain configurations that are not considered “optimal” configurationsmay be selected as candidate configurations. In embodiments, thepre-deployment module and repository 540 may determine the candidateconfigurations for pre-deployment, and may further select configurationsfor pre-deployment based on, for example:

-   -   Popularity (e.g., the number of VMs having a particular        configuration).    -   On-demand deployment time (e.g., the amount of time needed to        build a VM from scratch).    -   Resource cost (e.g., the amount of resources and/or the value of        the resources used to store a pre-deployed configuration), etc.

As an example, a relatively popular configuration would be more likelyto be pre-deployed than an unpopular configuration. Similarly, aconfiguration with a relatively long on-demand deployment time would bemore likely to be pre-deployed than a configuration with a relativelyshort on-demand deployment time. Similarly, a configuration with arelatively low resource cost would be more likely to be pre-deployedthan a configuration with a relatively low resource cost. Inembodiments, the pre-deployment module and repository 540 may determinethe resources consumed (i.e., the resource cost) by assigning weightingsto the resources consumed based on the types of resources. For example,CPU resources may be weighted differently than disk space resources. Inembodiments, the pre-deployment module and repository 540 may score thecandidate configurations for pre-deployment based on one or more of theabove factors, and may select configurations that exceed a particularscore. In alternative embodiments, the pre-deployment module andrepository 540 may identify commonalities between a set ofconfigurations (e.g., by sorting the configurations by base VM and bymost popular service). The pre-deployment module and repository 540 mayidentify the smallest significantly large group to form VM bases havingservices that are relatively common among a set of VMs.

In embodiments, the pre-deployment module and repository 540 maydetermine a quantity of copies of each configuration to pre-deploy(e.g., based on the available resources, the popularity/importance ofthe resources, and the quantity of copies that a configuration has beenpre-deployed to users). As an example, if the quantity of VMs with aparticular configuration to be deployed is anticipated or know to befewer than the quantity of configurations that are pre-deployed (e.g.,stored), then those copies of the pre-deployed configurations may beremoved to reclaim the resources used to pre-deploy the configurations.If the quantity of VMs with a particular configuration to be deployed isanticipated or know to be greater than the quantity of configurationsthat are pre-deployed (e.g., stored), then additional configurations arepre-deployed. In embodiments, the pre-deployment module and repository540 may pre-deploy configurations with the most popular services.

The post-deployment activity monitoring module 550 may include a programmodule (e.g., a program module 42 of FIG. 1) that may monitor theactivity of post-deployment VMs (e.g., VMs currently in use). Asdescribed herein, information regarding the activity of thepost-deployment VMs may be used to generate a catalog of automatedservices that are available to users of VMs, and dynamically adjust theautomated services as the activity of VMs changes. In embodiments, thepost-deployment activity monitoring module 550 may monitor activity,such as modifications made to VMs (e.g., applications/services added orremoved, user IDs created or removed, performing time synchronization,etc.), and monitoring the steps involved in implementing themodifications (e.g., monitoring scripts, cookbooks, installers, richsite summary (RSS) feeds, etc. used to implement the modifications). Inembodiments, monitored activity may include a command on a command lineinterface, a mouse click, a voice command, and/or any other action thatresults in a modification to a VM. In embodiments, the post-deploymentactivity monitoring module 550 may store VM information when amodification is detected (e.g., memory, CPUs, data storage information,etc.).

In embodiments, the post-deployment activity monitoring module 550 mayaccess external databases as part of monitoring the activity on a VM,thereby effectively “crowd sourcing” information identifying the usageof a VM. For example, the post-deployment activity monitoring module 550may access both public and private sources, such as blogs, social media,developer communities for cookbooks, etc. Also, the post-deploymentactivity monitoring module 550 may access information provided byvendors of software included in the VMs.

The automated services module 560 may include a program module (e.g., aprogram module 42 of FIG. 1) that generates a catalog of automatedservices that administrators or users may select to perform automatedtasks. The automated services module 560 may generate the automatedtasks based on the monitored activity of VMs, as determined by thepost-deployment activity monitoring module 550. In embodiments, theactivity of users may be non-linear in progression. The automatedservices module 560 may analyze certain actions/activity for relevanceto prior actions, and create links/paths between the different actions.Unrelated action paths may be separated or disconnected. Action pathsthat are determined to be related to existing services can be marked.Similarly, paths with an unknown relationship can also be marked. Pathlengths may be limited to keep further actions tractable.

In embodiments, the automated services module 560 may group monitoredactions based on similar services such that actions may be analyzed andtagged with fields (e.g., applications used, commands performed, etc.).Actions are analyzed for their similarities and differences, and groupedbased on similarities, and different actions may be used in uniqueconfigurations based on target VMs.

In embodiments, the automated services module 560 may generate automatedtasks based on monitored actions. In embodiments, the automated servicesmodule 560 may generate automated tasks based on a threshold number ofmodifications made across a group of VMs. For example, if a particularsoftware is installed greater than a threshold number of times, theautomated services module 560 may generate an automated task to installthis software, and store the automated task in a catalog of automatedtasks.

In embodiments, the automated services module 560 may test the automatedtasks to ensure that the automated tasks result in a desired outcome.For example, if a desired outcome was the installation of an anti-virus,and the test result indicates that no anti-virus is running, then theautomated services module 560 may determine that the automated task didnot result in a desired outcome, and the automated task me be discardedor modified. More complex testing may require that a service beinstalled within a specified time window or result in a certain level ofperformance. In embodiments, an administrator may review and/or manuallytest an automated task prior to release. From the catalog of automatedtasks, an administrator may select an automated task to automate themodification of VMs that are routinely manually performed (e.g., asdetermined by activity monitoring by the post-deployment activitymonitoring module 550). Also, the catalog of automated tasks isdynamically updated based on updated monitoring of VM usage activity.

FIG. 6 shows an example flowchart for presenting optimal configurationoptions to a requesting user of a new VM deployment. The steps of FIG. 6may be implemented in the environment of FIGS. 1-5, for example, and aredescribed using reference numbers of elements depicted in FIGS. 1-5. Asnoted above, the flowchart illustrates the architecture, functionality,and operation of possible implementations of systems, methods, andcomputer program products according to various embodiments of thepresent invention.

As shown in FIG. 6, process 600 may include receiving request for VMdeployment (step 610). For example, the optimal configuration andcatalog component 96 may receive the request for a new VM deploymentfrom a user of a client server 210. In embodiments, the request mayidentify the characteristics and/or configuration of the requested VM(e.g., the base VM, resources, services/applications, etc.).

Process 600 may also include storing the requested VM configuration(step 620). For example, the optimal configuration and catalog component96 may store the configuration for the requested VM (e.g., in the VMconfigurations repository 510). The requested configuration may be addedto a database of VM configurations of deployed VMs.

Process 600 may further include determining whether the requestedconfiguration is pre-deployed (step 630). For example, the optimalconfiguration and catalog component 96 may search within thepost-deployment activity monitoring module 550 to determine whether therequested configuration is pre-deployed (e.g., by comparing thecharacteristics of the requested configuration with the characteristicsof pre-deployed configurations). If, for example, the requestedconfiguration is pre-deployed (step 630-YES), then at step 640, process600 may include serving the VM to the user (e.g., providing access tothe VM by the client server 210 associated with the user). If, on theother hand, the requested configuration is not pre-deployed (step630-NO), process 600 may include building the VM on-demand (step 650).For example, the optimal configuration and catalog component 96 maybuild the VM from scratch in accordance with the requestedconfiguration. Process 600 may then proceed to step 640, as describedabove.

Process 600 may also include monitoring the performance and activity ofthe VM (step 660). For example, the optimal configuration and catalogcomponent 96 may monitor the performance and the activity of the VM(e.g., as described above with respect to the performance and usagemonitoring module 520).

Process 600 may further include periodically determining optimalconfiguration options based on the stored VM configurations and the VMperformance and activity information (step 670). For example, theoptimal configuration and catalog component 96 may periodicallydetermining optimal configuration options based on the stored VMconfigurations and the VM performance and activity information (e.g., asdescribed above with respect to the optimal configuration determinationmodule 530).

Process 600 may also include receiving a subsequent request for a VMdeployment (step 680). For example, the optimal configuration andcatalog component 96 may receive a subsequent request for a VMdeployment including a requested configuration of the VM.

Process 600 may further include presenting optimal configuration optionsto the user (step 690). For example, the optimal configuration andcatalog component 96 may present optimal configuration options to theuser (e.g., as described above with respect to the optimal configurationdetermination module 530). In embodiments, the optimal configurationoptions may include those configurations that have historicallyperformed above a performance measurement threshold and have similarcharacteristics/services as the services requested by the user at step680. Process 600 may return to step 610 after the optimal configurationoptions have been presented. For example, at step 610, the user mayselect one of the optimal configuration options, which may reduce theprobability that the VM would need to be re-deployed. As describedabove, the user may select a different configuration than the optimalconfiguration, if desired.

FIG. 7 shows an example flowchart for pre-deploying VMs with particularconfigurations. The steps of FIG. 7 may be implemented in theenvironment of FIGS. 1-5, for example, and are described using referencenumbers of elements depicted in FIGS. 1-5. As noted above, the flowchartillustrates the architecture, functionality, and operation of possibleimplementations of systems, methods, and computer program productsaccording to various embodiments of the present invention.

As shown in FIG. 7, process 700 may include storing informationregarding the configurations of deployed VMs (step 710). For example,the optimal configuration and catalog component 96 may store and/ormonitor information regarding the configurations of deployed VMs (e.g.,to select candidate configurations for pre-deployment based onpopularity as described above with respect to the pre-deployment moduleand repository 540).

Process 700 may further include monitoring the cost associated withon-demand VM building (step 720). For example, the optimal configurationand catalog component 96 may monitor the costs associated with buildinga VM, having a particular configuration, from scratch. In embodiments,the optimal configuration and catalog component 96 may monitor theamount of time and/or computing resources needed to build the VM fromscratch. Information regarding the cost may be used to determine whethera VM of a particular configuration should be pre-deployed.

Process 700 may also include scoring VM configurations forpre-deployment (step 730). For example, the optimal configuration andcatalog component 96 may score candidate VM configurations forpre-deployment based on the popularity of the VMs, the cost ofpre-deploying the VMs, the cost associated with building the VMs, ondemand, etc. In embodiments, the optimal configuration and catalogcomponent 96 may score candidate VM configurations as described abovewith respect to the pre-deployment module and repository 540.

Process 700 may further include pre-deploying VM configurations (step740). For example, the optimal configuration and catalog component 96may pre-deploy or store VM configurations whose score satisfies aparticular threshold. In embodiments, the optimal configuration andcatalog component 96 may pre-deploy or store VM configurations asdescribed above with respect to the pre-deployment module and repository540.

Process 700 may also include deleting pre-deployed VM configurations(step 750). For example, the optimal configuration and catalog component96 may periodically delete pre-deployed VM configurations that are nolonger popular (e.g., VM configurations whose measure of popularity ornumber of deployments falls below a particular threshold). Inembodiments, the optimal configuration and catalog component 96 maydelete pre-deployed VM configurations as described above with respect tothe pre-deployment module and repository 540.

FIG. 8 shows an example flowchart for pre-deploying VMs with particularconfigurations. The steps of FIG. 8 may be implemented in theenvironment of FIGS. 1-5, for example, and are described using referencenumbers of elements depicted in FIGS. 1-5. As noted above, the flowchartillustrates the architecture, functionality, and operation of possibleimplementations of systems, methods, and computer program productsaccording to various embodiments of the present invention.

As shown in FIG. 8, process 800 may include monitoring VM modificationactivity (step 810). For example, the optimal configuration and catalogcomponent 96 may monitor the VM modification activity as described abovewith respect to the post-deployment activity monitoring module 550.

Process 800 may also include determining techniques used to implementmodifications (step 820). For example, the optimal configuration andcatalog component 96 may determine techniques used to implementmodifications as described above with respect to the post-deploymentactivity monitoring module 550. In embodiments, the optimalconfiguration and catalog component 96 may determine techniques used toimplement modifications when a modification has been made greater than athreshold number of times (e.g., when a the optimal configuration andcatalog component 96 has identified modifications to a software packageacross deployed VMs greater than a threshold number of times). Inembodiments, the techniques used to implement the modifications may bereviewed by an administrator to validate the techniques. Additionally,or alternatively, the optimal configuration and catalog component 96 mayindicate to the administrator that no techniques to implement themodifications were found.

Process 800 may further include generating or updating an automatedaction based on the VM modification activity and the techniques used toimplement the modification (step 830). For example, the optimalconfiguration and catalog component 96 may generate or update anautomated action based on the VM modification activity and thetechniques used to implement the modification as described above withrespect to the automated services module 560.

Process 800 may also include storing an automated action in a catalog(step 840). For example, the optimal configuration and catalog component96 may store a newly generated automated action (or an update to anexisting automated action) in a catalog. In embodiments, the optimalconfiguration and catalog component 96 may store an automated action ina catalog described above with respect to the automated services module560.

In embodiments, an administrator may write automation for installationof any new services or modifications, or may simply review existingautomation techniques generated at step 830. In embodiments, automatedactions may be removed (e.g., when the automated actions are no longerused). An administrator may also be notified if the removal of anautomated action has reached a threshold number of VMs, and if theautomated action should be removed from the catalog altogether. Approvalof removal of automated actions from the catalog may also be automated.

In embodiments, the optimal configuration and catalog component 96 mayself-provision automated actions into particular VMs having particularconfigurations. The optimal configuration and catalog component 96 mayprovision multiple incarnations that modify items, such as CPU, memory,etc. to adjust/improve performance based on benchmark test runs. Inembodiments, an automated action may be verified or tested by anadministrator to check whether the automated action completedsuccessfully and/or whether installation of desired services completessuccessfully. Also, performance of a VM after implementing an automatedaction may be tested, and if the results satisfy a performance thresholdor performance metrics (e.g., no thrashing, uptime reliable, and/orother performance metrics) the automated action may be added to thecatalog of available automated actions. In embodiments, an automatedaction may be added to the catalog after a threshold number of VM'sadding or removing a service/software has been reached. Thus over time,automated actions can be used to ensure the most up-to-datesoftware/services are installed on a VM.

In embodiments, a service provider, such as a Solution Integrator, couldoffer to perform the processes described herein. In this case, theservice provider can create, maintain, deploy, support, etc., thecomputer infrastructure that performs the process steps of the inventionfor one or more customers. These customers may be, for example, anybusiness that uses technology. In return, the service provider canreceive payment from the customer(s) under a subscription and/or feeagreement and/or the service provider can receive payment from the saleof advertising content to one or more third parties.

In still additional embodiments, the invention provides acomputer-implemented method, via a network. In this case, a computerinfrastructure, such as computer system/server 12 (FIG. 1), can beprovided and one or more systems for performing the processes of theinvention can be obtained (e.g., created, purchased, used, modified,etc.) and deployed to the computer infrastructure. To this extent, thedeployment of a system can comprise one or more of: (1) installingprogram code on a computing device, such as computer system/server 12(as shown in FIG. 1), from a computer-readable medium; (2) adding one ormore computing devices to the computer infrastructure; and (3)incorporating and/or modifying one or more existing systems of thecomputer infrastructure to enable the computer infrastructure to performthe processes of the invention.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer-implemented method comprising:monitoring, by a computing device, performance of currently deployed(VMs) and VM modification activity of the currently deployed VMs;determining, based on the monitoring of the performance of the currentlydeployed VMs, one or more optimal configuration options; scoring, by thecomputing device, the one or more optimal configuration options, therebygenerating a score indicating a desirability of pre-deploying therespective one or more optimal configuration options; pre-deploying oneor more new VMs having at least one of the optimal configuration optionsbased on the scoring; serving, by the computing device, at least one ofthe pre-deployed one or more new VMs to a user; dynamically adjusting,by the computing device, a catalog of automated actions available tousers of the currently deployed VMs based on the monitoring of the VMmodification activity of the currently deployed VMs; and sending, by thecomputing device, a notification to an administrator that a removal of aprovisioned automated action from one or more VMs in the currentlydeployed VMs has reached a threshold number of VMs.
 2. Thecomputer-implemented method of claim 1, further comprising monitoringusage activity of the currently deployed VMs, wherein the determiningthe one or more optimal configuration options is further based on theusage activity.
 3. The computer-implemented method of claim 1, furthercomprising: receiving an instruction to deploy the select new VM; anddetermining that the select new VM has been pre-deployed.
 4. Thecomputer-implemented method claim 3, wherein the received instruction todeploy the select new VM includes an instruction to deploy the selectnew VM in accordance with one of the one or more optimal configurationoptions.
 5. The computer-implemented method of claim 1, wherein thedetermining the one or more optimal configuration options is based onreceiving a request for deployment of the one or more new VMs, whereinthe request includes a base VM and one or more requested services. 6.The computer-implemented method of claim 1, wherein the one or moreoptimal configuration options reduce a probability that the one or morenew VMs is redeployed with a different configuration.
 7. Thecomputer-implemented method of claim 1, wherein a service provider atleast one of creates, maintains, deploys and supports the computingdevice.
 8. The computer-implemented method of claim 1, wherein steps ofclaim 1 are provided by a service provider on a subscription,advertising, and/or fee basis.
 9. The computer-implemented method ofclaim 1, wherein the computing device includes software provided as aservice in a cloud environment.
 10. The computer-implemented method ofclaim 1, further comprising deploying a computer infrastructure operableto perform the steps of claim
 1. 11. The computer-implemented method ofclaim 1, wherein the scores are weighted based on types of theresources.
 12. The computer-implemented method of claim 1, wherein thepre-deploying includes determining a quantity of copies of each of theone or more new VMs to pre-deploy.
 13. The computer-implemented methodof claim 1, further comprising: determining, by the computing devicebased on the monitoring of the VM modification activity of the currentlydeployed VMs, modifications performed on a group of the currentlydeployed VMs; and generating, by the computing device, an automatedaction to automate one of the determined modifications performed on thegroup of the currently deployed VMs, wherein the dynamically adjustingthe catalog of automated actions available to users of the currentlydeployed VMs comprises adding the automated action to the catalog ofautomated actions.
 14. The computer-implemented method of claim 1,further comprising: determining, by the computing device based on themonitoring of the VM modification activity of the currently deployedVMs, modifications performed on the group of the currently deployed VMs,wherein the dynamically adjusting the catalog of automated actionsavailable to users of the currently deployed VMs comprises removing anautomated action from the catalog of automated actions based on thedetermining the modifications performed on the group of the currentlydeployed VMs.
 15. A computer program product comprising a computerreadable storage medium having program instructions embodied therewith,the program instructions executable by a computing device to cause thecomputing device to: monitor performance of currently deployed VMs;determine one or more optimal configuration options based on themonitored performance of the currently deployed VMs, wherein thedetermining the one or more optimal configuration options comprisesidentifying one or more of the currently deployed VMs that satisfy aperformance threshold, wherein the one or more optimal configurationoptions are configurations of the one or more of the currently deployedVMs; determine a score for each of the one or more optimal configurationoptions, wherein each score indicates a desirability of pre-deployingthe respective one or more optimal configuration options, and each scoreis based on a popularity of one or more services implemented by each ofthe one or more of the currently deployed VMs, wherein each score isfurther based on a cost associated with resources consumed by each ofthe one or more of the currently deployed VMs, and wherein thedetermining the score further includes applying weightings based ontypes of the resources consumed by each of the one or more currentlydeployed VMs; pre-deploy one or more new VMs having a subset of the oneor more optimal configuration options that have scores meeting athreshold; serve at least one of the pre-deployed one or more new VMs toa user; determine modifications performed on a group of the currentlydeployed VMs by monitoring VM modification activity of the group of thecurrently deployed VMs; generate an automated action to automate one ofthe determined modifications performed on the group of the currentlydeployed VMs; provision the automated action into one or more VMs in thegroup of the currently deployed VMs; continue to monitor the VMmodification activity of the group of the currently deployed VMs; anddynamically adjust a catalog of automated actions available to users ofthe group of currently deployed VMs based on the continued monitoring ofthe VM modification activity of the group of the currently deployed VMs.16. The computer program product of claim 15, wherein the programinstructions further cause the computing device to determine the costassociated with the resources consumed by each of the one or more of thecurrently deployed VMs, the determining the cost including eliminatingfactors that do not impact the costs.
 17. The computer program productof claim 15, wherein the program instructions further cause thecomputing device to: periodically update the scores of the one or moreoptimal configurations based on changes to at least one of thepopularity and the cost; and remove one or more new VMs frompre-deployment that have scores that are below a particular threshold.18. The computer program product of claim 15, wherein the programinstructions further cause the computing device to output an option tothe user to select a new VM of the one or more new VMs for deployment,wherein the serving the pre-deployed one or more new VMs to the user isbased on receiving a selection from the user.